Meta-Analysis is the gold standard statistical approach to combine the results of multiple studies and to examine sources of heterogeneity and potential biases. This course will review fixed-effect and random-effects models that underlie the combination of study results in meta-analysis; the use of study-level predictors in meta-regression; assessment of small-study effects and related reporting biases; and sensitivity analyses to bias. Examples will cover meta-analysis of randomized trials and of observational studies. Throughout the course, participants will apply each model by using the R software.
Participants will learn to:
- Utilize the fixed-effect and random-effects methods of combining effect sizes
- Describe different ways to measure between-study heterogeneity
- Describe the strengths and weaknesses of random-effects as compared to fixed effect meta-analysis
- Assess the potential impact of small-study effects and related reporting biases on a combined effect size estimate
- Perform meta-regression modeling and describe the limitations of meta-regression
The target audience includes biostatisticians, data analysts, and quantitative researchers from academia, the pharmaceutical industry, and other government institutions.
Basic knowledge of study design and regression modeling, and a basic working knowledge of R are necessary to be successful in the course. Participants must bring a laptop to the class sessions.